To cite the sparse-group SLOPE method in publications use

Feser F, Evangelou M (2023). “Sparse-group SLOPE: adaptive bi-level selection with FDR-control.” arXiv. doi:10.48550/arXiv.2305.09467, https://arxiv.org/abs/2305.09467.

Feser F, Evangelou M (2025). “Strong Screening Rules for Group-based SLOPE Models.” In Li Y, Mandt S, Agrawal S, Khan E (eds.), Proceedings of The 28th International Conference on Artificial Intelligence and Statistics, volume 258 series Proceedings of Machine Learning Research, 352–360. https://proceedings.mlr.press/v258/feser25a.html.

To cite the sgs R package in publications use:

Feser F (2023). sgs. https://CRAN.R-project.org/package=sgs.

Corresponding BibTeX entries:

  @Article{,
    title = {Sparse-group SLOPE: adaptive bi-level selection with
      FDR-control},
    author = {Fabio Feser and Marina Evangelou},
    journal = {arXiv},
    year = {2023},
    doi = {10.48550/arXiv.2305.09467},
    url = {https://arxiv.org/abs/2305.09467},
  }

  @InProceedings{,
    title = {Strong Screening Rules for Group-based SLOPE Models},
    author = {Fabio Feser and Marina Evangelou},
    booktitle = {Proceedings of The 28th International Conference on
      Artificial Intelligence and Statistics},
    pages = {352--360},
    year = {2025},
    editor = {Yingzhen Li and Stephan Mandt and Shipra Agrawal and
      Emtiyaz Khan},
    volume = {258},
    series = {Proceedings of Machine Learning Research},
    month = {03--05 May},
    publisher = {PMLR},
    pdf =
      {https://raw.githubusercontent.com/mlresearch/v258/main/assets/feser25a/feser25a.pdf},
    url = {https://proceedings.mlr.press/v258/feser25a.html},
    abstract = {Tuning the regularization parameter in penalized
      regression models is an expensive task, requiring multiple models
      to be fit along a path of parameters. Strong screening rules
      drastically reduce computational costs by lowering the
      dimensionality of the input prior to fitting. We develop strong
      screening rules for group-based Sorted L-One Penalized Estimation
      (SLOPE) models: Group SLOPE and Sparse-group SLOPE. The developed
      rules are applicable to the wider family of group-based OWL
      models, including OSCAR. Our experiments on both synthetic and
      real data show that the screening rules significantly accelerate
      the fitting process. The screening rules make it accessible for
      group SLOPE and sparse-group SLOPE to be applied to
      high-dimensional datasets, particularly those encountered in
      genetics.},
  }

  @Manual{,
    title = {sgs},
    author = {Fabio Feser},
    year = {2023},
    url = {https://CRAN.R-project.org/package=sgs},
  }